Instructions to use mychen76/mistral-7b-merged-dare_6x7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mychen76/mistral-7b-merged-dare_6x7 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mychen76/mistral-7b-merged-dare_6x7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mychen76/mistral-7b-merged-dare_6x7")
model = AutoModelForCausalLM.from_pretrained("mychen76/mistral-7b-merged-dare_6x7", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mychen76/mistral-7b-merged-dare_6x7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mychen76/mistral-7b-merged-dare_6x7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mychen76/mistral-7b-merged-dare_6x7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mychen76/mistral-7b-merged-dare_6x7

SGLang

How to use mychen76/mistral-7b-merged-dare_6x7 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mychen76/mistral-7b-merged-dare_6x7" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mychen76/mistral-7b-merged-dare_6x7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mychen76/mistral-7b-merged-dare_6x7" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mychen76/mistral-7b-merged-dare_6x7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mychen76/mistral-7b-merged-dare_6x7 with Docker Model Runner:
```
docker model run hf.co/mychen76/mistral-7b-merged-dare_6x7
```

mistral-7b-merged-dare-v2

mistral-7b-merged-dare-v2 is a merge of the following models:

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
  - model: samir-fama/SamirGPT-v1
    parameters:
      density: 0.53
      weight: 0.4
  - model: abacusai/Slerp-CM-mist-dpo
    parameters:
      density: 0.53
      weight: 0.3
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
    parameters:
      density: 0.53
      weight: 0.3
  - model: Weyaxi/Einstein-v4-7B
    parameters:
      density: 0.53
      weight: 0.3            
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mistral-7b-merged-dare_6x7"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Why the sky is blue"}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.46
AI2 Reasoning Challenge (25-Shot)	69.62
HellaSwag (10-Shot)	87.04
MMLU (5-Shot)	65.18
TruthfulQA (0-shot)	66.98
Winogrande (5-shot)	80.58
GSM8k (5-shot)	71.34

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Safetensors

Model size

7B params

Tensor type

BF16

Collection including mychen76/mistral-7b-merged-dare_6x7

MoE & Merge

Collection

13 items • Updated Apr 23 • 1